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Join us for an unmissable evening of insight, discussion, and lively debate at The High Performance Data and AI Debate, hosted by Chris Tabb — a unique Big Data London special running from 6:00–8:00 PM. This fast-paced, interactive event brings together some of the brightest minds in data and AI to tackle the most pressing questions shaping the future of teams, architecture, and products in an AI-first world.

The evening kicks off at 6:00 PM with a welcome and free drinks. Then, across three rapid-fire 20-minute debates, our expert panels will explore:

• AI & Data – Teams (Chair: Eevamaija Virtanen)

Mehdi Ouazza, Paul Rankin, Jesse Anderson, Hugo Lu

• AI & Data – Architecture (Chair: Adi Polak)

Chris Freestone, David Richardson, Nick White, Karl Ivo Sokolov

• AI & Data – Products (Chair: Jai Parmar)

Kelsey Hammock, Jean-Georges (jgp) Perrin, Taylor McGrath, Jon Cooke

Refuel with free pizza at 6:50 PM, then stay for the Town Hall Debate, where all speakers return to the stage for an open-floor Q&A — your chance to challenge their ideas, share perspectives, and shape the conversation.

Expect fresh perspectives, healthy disagreement, and practical takeaways you can bring back to your organisation. Whether you’re leading a data team, designing cutting-edge architectures, or building AI-powered products, this is your space to engage with the people shaping what’s next.

6:00 pm - Intro & Drinks hosted by Chris Tabb

6:10 pm - Session One - High Performance Data Products

David Richardson, Jon Cooke, Taylor McGrath, Mark van der Heijden

6:30pm - Session Two - High Performance Data Models

Joe Reis 🤓, Keith Belanger, Nick White, Eevamaija Virtanen

6:50pm - Pizza and Drinks 🍕🥤🍷🍻

7:00pm - Session Three - High Performance AI

Alex Chung, Jai Parmar, Sonny Rivera, Addie McNamara

7:20pm - Town Hall Debate

Sponsors: Coalesce, LEIT DATA, Rivery, SqlDBM, ThoughtSpot 

Data products are a very popular topic these days. The challenge is we need new thinking and approaches that differ from how we've worked with data in the past. Jon Cooke and I chat about the mindset shift needed to make data products successful.

Jon Cooke's LinkedIn: https://www.linkedin.com/in/jon-cooke-096bb0/

If you like this show, give it a 5-star rating on your favorite podcast platform.

Purchase Fundamentals of Data Engineering at your favorite bookseller.

Subscribe to my Substack: https://joereis.substack.com/

Today I’m sitting down with Jon Cooke, founder and CTO of Dataception, to learn his definition of a data product and his views on generating business value with your data products. In our conversation, Jon explains his philosophy on data products and where design and UX fit in. We also review his conceptual model for data products (which he calls the data product pyramid), and discuss how together, these concepts allow teams to ship working solutions faster that actually produce value. 

Highlights/ Skip to:

Jon’s definition of a data product (1:19)  Brian explains how UX research and design planning can and should influence data architecture —so that last mile solutions are useful and usable (9:47) The four characteristics of a data product in Jon’s model (16:16) The idea of products having a lifecycle with direct business/customer interaction/feedback (17:15) Understanding Jon’s data product pyramid (19:30) The challenges when customers/users don’t know what they want from data product teams - and who should be doing the work to surface requirements (24:44) Mitigating risk and the importance of having management buy-in when adopting a product-driven approach (33:23) Does the data product pyramid account for UX? (35:02) What needs to change in an org model that produces data products that aren’t delivering good last mile UXs (39:20)

Quotes from Today’s Episode “A data product is something that specifically solves a business problem, a piece of analytics, data use case, a pipeline, datasets, dashboard, that type that solves a business use case, and has a customer, and as a product lifecycle to it.” - Jon (2:15)

“I’m a fan of any definition that includes some type of deployment and use by some human being. That’s the end of the cycle, because the idea of a product is a good that has been made, theoretically, for sale.” - Brian (5:50)

“We don’t build a lot of stuff around cloud anymore. We just don’t build it from scratch. It’s like, you know, we don’t generate our own electricity, we don’t mill our own flour. You know, the cloud—there’s a bunch of composable services, which I basically pull together to build my application, whatever it is. We need to apply that thinking all the way through the stack, fundamentally.” - Jon (13:06)

“It’s not a data science problem, it’s not a business problem, it’s not a technology problem, it’s not a data engineering problem, it’s an everyone problem. And I advocate small, multidisciplinary teams, which have a business value person in it, have an SME, have a data scientist, have a data architect, have a data engineer, as a small pod that goes in and answer those questions.” - Jon (26:28)

“The idea is that you’re actually building the data products, which are the back-end, but you’re actually then also doing UX alongside that, you know? You’re doing it in tandem.” - Jon (37:36)

“Feasibility is one of the legs of the stools. There has to be market need, and your market just may be the sales team, but there needs to be some promise of value there that this person is really responsible for at the end of the day, is this data product going to create value or not?” - Brian (42:35)

“The thing about data products is sometimes you don’t know how feasible it is until you actually look at the data…You’ve got to do what we call data archaeology. You got to go and find the data, you got to brush it off, and you’re looking at and go, ‘Is it complete?’” - Jon (44:02)

Links Referenced: Dataception Data Product Pyramid Email: [email protected] LinkedIn: https://www.linkedin.com/in/jon-cooke-096bb0/